Machine Learning
2024年1月11日
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1 分钟阅读时长
Overview
We build robust and interpretable ML models to accelerate scientific discovery across domains, with emphasis on generalization, uncertainty, and physical constraints.
Focus Areas
- Physics-informed neural networks (PINNs)
- Graph neural networks for structured scientific data
- Interpretable models and reliability assessment
Selected Publications
- See our recent work in Nature Machine Intelligence and NeurIPS.